AI-based workflows
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ML-based workflows
We validate our ML workflows across independent data scientists with pathways in different programming languages.
- Assessing the best ML workflow
- Comparing decision tree analyses (Ensemble tools) with simpler regression models by integrating bootstrapping.
- Integration of image, clinical, geopositional or biomarker data
- Interpretation with our own and existing datasets
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Generative AI
Generative AI offers the capacity to map a virtually unlimited number of synthetic individuals with biomarker profiles that follow the distribution of biomarkers in real-life samples.
- Synthetic clones
- Augmented data in model training
- Enhanced predictions
- Multi-context applicability
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Predictive analysis
Utilising clinical, biochemical, molecular, socio-economic, geopositional and other relevant data to drive accurate prediction of future health.
- Study design
- Suitable AI/ML strategies
- Use of our tissue/data bank
- Predictive analyses and independent validation.
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Personalised medicine
Our AI-based workflows are designed to drive personalised decision making, cohort-based risk stratification as well as context (ethnicity/race)-driven predictive risk scores.
- Context-driven predictions
- Dynamic risk scores
- Static risk score assessment
- Integrated learning for personalised solutions to healthcare
All codes for workflows used/published by our group are available through Github/isletbiology/ or through collaborative arrangements and support by contacting Professor Hardikar.
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